






"""simple docstring"""





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]						,							_lowerCamelCase			:						Optional[int]						,							_lowerCamelCase			:						Tuple					) ->							Dict:
  if isinstance(_lowerCamelCase						,							torch.Tensor					):
    return image
  elif isinstance(_lowerCamelCase						,							PIL.Image.Image					):
    lowerCamelCase_       	=		[image]

  if isinstance(image[0]						,							PIL.Image.Image					):
    lowerCamelCase_       	=		[np.array(i.resize((w, h)						,							resample=PIL_INTERPOLATION['lanczos']					)					)[None, :] for i in image]
    lowerCamelCase_       	=		np.concatenate(_lowerCamelCase						,							axis=0					)
    lowerCamelCase_       	=		np.array(_lowerCamelCase					).astype(np.floataa					) / 2_55.0
    lowerCamelCase_       	=		image.transpose(0						,							3						,							1						,							2					)
    lowerCamelCase_       	=		2.0 * image - 1.0
    lowerCamelCase_       	=		torch.from_numpy(_lowerCamelCase					)
  elif isinstance(image[0]						,							torch.Tensor					):
    lowerCamelCase_       	=		torch.cat(_lowerCamelCase						,							dim=0					)
  return image







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						str						,							_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						Any=0.99_95					) ->							int:
  if not isinstance(_lowerCamelCase						,							np.ndarray					):
    lowerCamelCase_       	=		True
    lowerCamelCase_       	=		va.device
    lowerCamelCase_       	=		va.cpu().numpy()
    lowerCamelCase_       	=		va.cpu().numpy()

  lowerCamelCase_       	=		np.sum(va * va / (np.linalg.norm(_lowerCamelCase					) * np.linalg.norm(_lowerCamelCase					))					)
  if np.abs(_lowerCamelCase					) > DOT_THRESHOLD:
    lowerCamelCase_       	=		(1 - t) * va + t * va
  else:
    lowerCamelCase_       	=		np.arccos(_lowerCamelCase					)
    lowerCamelCase_       	=		np.sin(_lowerCamelCase					)
    lowerCamelCase_       	=		theta_a * t
    lowerCamelCase_       	=		np.sin(_lowerCamelCase					)
    lowerCamelCase_       	=		np.sin(theta_a - theta_t					) / sin_theta_a
    lowerCamelCase_       	=		sin_theta_t / sin_theta_a
    lowerCamelCase_       	=		sa * va + sa * va

  if inputs_are_torch:
    lowerCamelCase_       	=		torch.from_numpy(_lowerCamelCase					).to(_lowerCamelCase					)

  return va







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Union[str, Any]					) ->							List[Any]:
  lowerCamelCase_       	=		F.normalize(_lowerCamelCase						,							dim=-1					)
  lowerCamelCase_       	=		F.normalize(_lowerCamelCase						,							dim=-1					)
  return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Any					) ->							Optional[int]:
  for param in model.parameters():
    lowerCamelCase_       	=		value






class 	a		(   __snake_case						):






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				AutoencoderKL			,		__SCREAMING_SNAKE_CASE	:				CLIPTextModel			,		__SCREAMING_SNAKE_CASE	:				CLIPModel			,		__SCREAMING_SNAKE_CASE	:				CLIPTokenizer			,		__SCREAMING_SNAKE_CASE	:				UNetaDConditionModel			,		__SCREAMING_SNAKE_CASE	:				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]			,		__SCREAMING_SNAKE_CASE	:				CLIPFeatureExtractor			,		__SCREAMING_SNAKE_CASE	:				List[Any]=None			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=None			,		__SCREAMING_SNAKE_CASE	:				Any=None			,		)	->						Union[str, Any]:
      super().__init__()
      self.register_modules(
          vae=__SCREAMING_SNAKE_CASE			,		text_encoder=__SCREAMING_SNAKE_CASE			,		clip_model=__SCREAMING_SNAKE_CASE			,		tokenizer=__SCREAMING_SNAKE_CASE			,		unet=__SCREAMING_SNAKE_CASE			,		scheduler=__SCREAMING_SNAKE_CASE			,		feature_extractor=__SCREAMING_SNAKE_CASE			,		coca_model=__SCREAMING_SNAKE_CASE			,		coca_tokenizer=__SCREAMING_SNAKE_CASE			,		coca_transform=__SCREAMING_SNAKE_CASE			,		)
      lowerCamelCase_       	=		(
          feature_extractor.size
          if isinstance(feature_extractor.size			,		__SCREAMING_SNAKE_CASE   )
          else feature_extractor.size['shortest_edge']
      )
      lowerCamelCase_       	=		transforms.Normalize(mean=feature_extractor.image_mean			,		std=feature_extractor.image_std   )
      set_requires_grad(self.text_encoder			,		__SCREAMING_SNAKE_CASE   )
      set_requires_grad(self.clip_model			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, int]] = "auto"   )	->						Union[str, Any]:
      if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        lowerCamelCase_       	=		self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      self.enable_attention_slicing(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[str]:
      set_requires_grad(self.vae			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[int]:
      set_requires_grad(self.vae			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Any:
      set_requires_grad(self.unet			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						List[Any]:
      set_requires_grad(self.unet			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str   )	->						Dict:
      # get the original timestep using init_timestep
      lowerCamelCase_       	=		min(int(num_inference_steps * strength   )			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		max(num_inference_steps - init_timestep			,		0   )
      lowerCamelCase_       	=		self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				int=None   )	->						Optional[Any]:
      if not isinstance(__SCREAMING_SNAKE_CASE			,		torch.Tensor   ):
        raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE   )}'''   )

      lowerCamelCase_       	=		image.to(device=__SCREAMING_SNAKE_CASE			,		dtype=__SCREAMING_SNAKE_CASE   )

      if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		[
            self.vae.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(__SCREAMING_SNAKE_CASE   )
        ]
        lowerCamelCase_       	=		torch.cat(__SCREAMING_SNAKE_CASE			,		dim=0   )
      else:
        lowerCamelCase_       	=		self.vae.encode(__SCREAMING_SNAKE_CASE   ).latent_dist.sample(__SCREAMING_SNAKE_CASE   )

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      lowerCamelCase_       	=		0.18_215 * init_latents
      lowerCamelCase_       	=		init_latents.repeat_interleave(__SCREAMING_SNAKE_CASE			,		dim=0   )

      lowerCamelCase_       	=		randn_tensor(init_latents.shape			,		generator=__SCREAMING_SNAKE_CASE			,		device=__SCREAMING_SNAKE_CASE			,		dtype=__SCREAMING_SNAKE_CASE   )

      # get latents
      lowerCamelCase_       	=		self.scheduler.add_noise(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		init_latents

      return latents






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Tuple:
      lowerCamelCase_       	=		self.coca_transform(__SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
      with torch.no_grad(), torch.cuda.amp.autocast():
        lowerCamelCase_       	=		self.coca_model.generate(transformed_image.to(device=self.device			,		dtype=self.coca_model.dtype   )   )
      lowerCamelCase_       	=		self.coca_tokenizer.decode(generated[0].cpu().numpy()   )
      return generated.split('<end_of_text>'   )[0].replace('<start_of_text>'			,		''   ).rstrip(' .,'   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						List[Any]:
      lowerCamelCase_       	=		self.feature_extractor.preprocess(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		torch.from_numpy(clip_image_input['pixel_values'][0]   ).unsqueeze(0   ).to(self.device   ).half()
      lowerCamelCase_       	=		self.clip_model.get_image_features(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image_embeddings_clip / image_embeddings_clip.norm(p=2			,		dim=-1			,		keepdim=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image_embeddings_clip.repeat_interleave(__SCREAMING_SNAKE_CASE			,		dim=0   )
      return image_embeddings_clip






    @torch.enable_grad()
    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		)	->						Optional[Any]:
      lowerCamelCase_       	=		latents.detach().requires_grad_()

      lowerCamelCase_       	=		self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      # predict the noise residual
      lowerCamelCase_       	=		self.unet(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

      if isinstance(self.scheduler			,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
        lowerCamelCase_       	=		self.scheduler.alphas_cumprod[timestep]
        lowerCamelCase_       	=		1 - alpha_prod_t
        # compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        lowerCamelCase_       	=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

        lowerCamelCase_       	=		torch.sqrt(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		self.scheduler.sigmas[index]
        lowerCamelCase_       	=		latents - sigma * noise_pred
      else:
        raise ValueError(F'''scheduler type {type(self.scheduler   )} not supported'''   )

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      lowerCamelCase_       	=		1 / 0.18_215 * sample
      lowerCamelCase_       	=		self.vae.decode(__SCREAMING_SNAKE_CASE   ).sample
      lowerCamelCase_       	=		(image / 2 + 0.5).clamp(0			,		1   )

      lowerCamelCase_       	=		transforms.Resize(self.feature_extractor_size   )(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.normalize(__SCREAMING_SNAKE_CASE   ).to(latents.dtype   )

      lowerCamelCase_       	=		self.clip_model.get_image_features(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image_embeddings_clip / image_embeddings_clip.norm(p=2			,		dim=-1			,		keepdim=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		spherical_dist_loss(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ).mean() * clip_guidance_scale

      lowerCamelCase_       	=		-torch.autograd.grad(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )[0]

      if isinstance(self.scheduler			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		latents.detach() + grads * (sigma**2)
        lowerCamelCase_       	=		noise_pred_original
      else:
        lowerCamelCase_       	=		noise_pred_original - torch.sqrt(__SCREAMING_SNAKE_CASE   ) * grads
      return noise_pred, latents






    @torch.no_grad()
    def __call__(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Union[torch.FloatTensor, PIL.Image.Image]			,		__SCREAMING_SNAKE_CASE	:				Union[torch.FloatTensor, PIL.Image.Image]			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = 512			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = 512			,		__SCREAMING_SNAKE_CASE	:				float = 0.6			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = 50			,		__SCREAMING_SNAKE_CASE	:				Optional[float] = 7.5			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = 1			,		__SCREAMING_SNAKE_CASE	:				float = 0.0			,		__SCREAMING_SNAKE_CASE	:				Optional[float] = 100			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.Generator] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = "pil"			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				float = 0.8			,		__SCREAMING_SNAKE_CASE	:				float = 0.1			,		__SCREAMING_SNAKE_CASE	:				float = 0.1			,		)	->						Dict:
      if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) and len(__SCREAMING_SNAKE_CASE   ) != batch_size:
        raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(__SCREAMING_SNAKE_CASE   )} generators.'''   )

      if height % 8 != 0 or width % 8 != 0:
        raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

      if isinstance(__SCREAMING_SNAKE_CASE			,		torch.Generator   ) and batch_size > 1:
        lowerCamelCase_       	=		[generator] + [None] * (batch_size - 1)

      lowerCamelCase_       	=		[
          ('model', self.coca_model is None),
          ('tokenizer', self.coca_tokenizer is None),
          ('transform', self.coca_transform is None),
      ]
      lowerCamelCase_       	=		[x[0] for x in coca_is_none if x[1]]
      lowerCamelCase_       	=		', '.join(__SCREAMING_SNAKE_CASE   )
      # generate prompts with coca model if prompt is None
      if content_prompt is None:
        if len(__SCREAMING_SNAKE_CASE   ):
          raise ValueError(
              F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
              F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''   )
        lowerCamelCase_       	=		self.get_image_description(__SCREAMING_SNAKE_CASE   )
      if style_prompt is None:
        if len(__SCREAMING_SNAKE_CASE   ):
          raise ValueError(
              F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
              F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''   )
        lowerCamelCase_       	=		self.get_image_description(__SCREAMING_SNAKE_CASE   )

      # get prompt text embeddings for content and style
      lowerCamelCase_       	=		self.tokenizer(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=self.tokenizer.model_max_length			,		truncation=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'			,		)
      lowerCamelCase_       	=		self.text_encoder(content_text_input.input_ids.to(self.device   )   )[0]

      lowerCamelCase_       	=		self.tokenizer(
          __SCREAMING_SNAKE_CASE			,		padding='max_length'			,		max_length=self.tokenizer.model_max_length			,		truncation=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'			,		)
      lowerCamelCase_       	=		self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

      lowerCamelCase_       	=		slerp(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      # duplicate text embeddings for each generation per prompt
      lowerCamelCase_       	=		text_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE			,		dim=0   )

      # set timesteps
      lowerCamelCase_       	=		'offset' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
      lowerCamelCase_       	=		{}
      if accepts_offset:
        lowerCamelCase_       	=		1

      self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      self.scheduler.timesteps.to(self.device   )

      lowerCamelCase_	,      lowerCamelCase_       	=		self.get_timesteps(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		self.device   )
      lowerCamelCase_       	=		timesteps[:1].repeat(__SCREAMING_SNAKE_CASE   )

      # Preprocess image
      lowerCamelCase_       	=		preprocess(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.prepare_latents(
          __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		text_embeddings.dtype			,		self.device			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		preprocess(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.prepare_latents(
          __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		text_embeddings.dtype			,		self.device			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		slerp(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      if clip_guidance_scale > 0:
        lowerCamelCase_       	=		self.get_clip_image_embeddings(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		self.get_clip_image_embeddings(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		slerp(
            __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      lowerCamelCase_       	=		guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
        lowerCamelCase_       	=		content_text_input.input_ids.shape[-1]
        lowerCamelCase_       	=		self.tokenizer(['']			,		padding='max_length'			,		max_length=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   )
        lowerCamelCase_       	=		self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
        # duplicate unconditional embeddings for each generation per prompt
        lowerCamelCase_       	=		uncond_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE			,		dim=0   )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        lowerCamelCase_       	=		torch.cat([uncond_embeddings, text_embeddings]   )

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      lowerCamelCase_       	=		(batch_size, self.unet.config.in_channels, height // 8, width // 8)
      lowerCamelCase_       	=		text_embeddings.dtype
      if latents is None:
        if self.device.type == "mps":
          # randn does not work reproducibly on mps
          lowerCamelCase_       	=		torch.randn(__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		device='cpu'			,		dtype=__SCREAMING_SNAKE_CASE   ).to(
              self.device   )
        else:
          lowerCamelCase_       	=		torch.randn(__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		device=self.device			,		dtype=__SCREAMING_SNAKE_CASE   )
      else:
        if latents.shape != latents_shape:
          raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
        lowerCamelCase_       	=		latents.to(self.device   )

      # scale the initial noise by the standard deviation required by the scheduler
      lowerCamelCase_       	=		latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      lowerCamelCase_       	=		'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
      lowerCamelCase_       	=		{}
      if accepts_eta:
        lowerCamelCase_       	=		eta

      # check if the scheduler accepts generator
      lowerCamelCase_       	=		'generator' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
      if accepts_generator:
        lowerCamelCase_       	=		generator

      with self.progress_bar(total=__SCREAMING_SNAKE_CASE   ):
        for i, t in enumerate(__SCREAMING_SNAKE_CASE   ):
          # expand the latents if we are doing classifier free guidance
          lowerCamelCase_       	=		torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
          lowerCamelCase_       	=		self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

          # predict the noise residual
          lowerCamelCase_       	=		self.unet(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

          # perform classifier free guidance
          if do_classifier_free_guidance:
            lowerCamelCase_	,      lowerCamelCase_       	=		noise_pred.chunk(2   )
            lowerCamelCase_       	=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # perform clip guidance
          if clip_guidance_scale > 0:
            lowerCamelCase_       	=		(
                text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
            )
            lowerCamelCase_	,      lowerCamelCase_       	=		self.cond_fn(
                __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		)

          # compute the previous noisy sample x_t -> x_t-1
          lowerCamelCase_       	=		self.scheduler.step(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      lowerCamelCase_       	=		1 / 0.18_215 * latents
      lowerCamelCase_       	=		self.vae.decode(__SCREAMING_SNAKE_CASE   ).sample

      lowerCamelCase_       	=		(image / 2 + 0.5).clamp(0			,		1   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   ).numpy()

      if output_type == "pil":
        lowerCamelCase_       	=		self.numpy_to_pil(__SCREAMING_SNAKE_CASE   )

      if not return_dict:
        return (image, None)

      return StableDiffusionPipelineOutput(images=__SCREAMING_SNAKE_CASE			,		nsfw_content_detected=__SCREAMING_SNAKE_CASE   )












"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




